Enhancing Camera Operator Performance with Computer Vision Based Control
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32799
Enhancing Camera Operator Performance with Computer Vision Based Control

Authors: Paul Y. Oh, Rares I. Stanciu

Abstract:

Cameras are often mounted on platforms that canmove like rovers, booms, gantries and aircraft. People operate suchplatforms to capture desired views of scene or target. To avoidcollisions with the environment and occlusions, such platforms oftenpossess redundant degrees-of-freedom. As a result, manipulatingsuch platforms demands much skill. Visual-servoing some degrees-of-freedom may reduce operator burden and improve tracking per-formance. This concept, which we call human-in-the-loop visual-servoing, is demonstrated in this paper and applies a Α-β-γ filter and feedforward controller to a broadcast camera boom.

Keywords: Computer vision, visual-servoing, man-machine sys-tems, human-in-the-loop control

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330193

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[1] Canon, D.J. (1994). Experiments With a Target-Threshold ControlTheory Model for Deriving Fitts Law Parameters for Human-MachineWorld Academy of Science, Engineering and Technology 2 2007507Fig. 9. The PTU Bode magnitude (top) and phase (bottom) plotsFig. 10. Tracking errors comparing feedforward and proportional control inhuman-in-the-loop visual-servoing.World Academy of Science, Engineering and Technology 2 2007508Systems?, IEEE Trans on Systems, Man and Cybernetics, V24 N8, pp.1089-1098.
[2] Chaumette F., Rives P., Espiau B. (1991). ?Positioning of a robot withRespect to an Object, Track it and Estimating its Velocity by VisualServoing?, IEEE Int Conf Robotics and Automation (ICRA), Sacramento,CA.
[3] Corke P.I., Good M.C. (1996). ?Dynamic Effects in Visual Closed-LoopSystems? IEEE Trans on Robotics and Automation V12 N5.
[4] Hutchinson S., Hager G.D., Corke P.I. (1996). ?A Tutorial on VisualServo Control?, IEEE Trans on Robotics and Automation V12 N5, pp.651-670.
[5] Isard, M., Blake, A. (1998). ?CONDENSATION ? Conditional DensityPropagation for Visual Tracking?, Int. J. Computer Vision, V29, N1, pp.5-28.
[6] Kalata, P.R., Murphy, K.M. (1997). ? Target Tracking with TrackRate Variations?, Proc of the Twenty-Ninth Southeastern Symposium onSystem Theory, pp. 70-74.
[7] Oh, P.Y., Allen, P.K. (2001). ?Visual Servoing by Partitioning Degreesof Freedom?, IEEE Trans on Robotics Automation V17 N1, pp. 1-17.
[8] Sheridan T.B., Ferrell W.R. (1994). Man-Machine Systems: Information,Control, and Decision Models of Human Performance, MIT Press,Cambridge, Massachusetts.
[9] Stanciu R., Oh P.Y. (2002). ?Designing Visually Servoed Tracking toAugment Camera Teleoperators? IEEE Intelligent Robots and System(IROS), Lausanne, Switzerland, V1, pp. 342-347.
[10] Stanciu R., Oh P.Y. (2003). ?Human-in-the-loop Visually ServoedTracking? International Conference on Computer, Communication andControl Technologies (CCCT), V5, pp. 318-323, Orlando, FL.
[11] Tenne, D., Singh, T. (2000). ?Optimal Design of Filters?,Proc of the American Control Conference, V6 pp. 4348-4352.World Academy of Science, Engineering and Technology 2 2007509